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Robot Path Planning using Dynamic Programming with Accelerating Nodes

Paladyn 2012年第1期3卷 23-34页

作者： Kala, Rahul Shukla, Anupam Tiwari, Ritu Soft Computing and Expert System Laboratory Indian Institute of Information Technology and Management Gwalior Madhya Pradesh Gwalior474010 India

We solve the problem of robot path planning using Dynamic Programming (DP) designed to perform well in case of a sudden path blockage. A conventional DP algorithm works well for real time scenarios only when the update frequency is high i.e. changes can be readily propagated. In case updates are costly, for a sudden blockage the robot continues moving along the wrong path or stands stationary. We propose a modified DP that has nodes with additional processing (called accelerating nodes) to enable different segments of the map to become informed about the blockage rapidly. We further quickly compute an alternative path in case of a blockage. Experimental results verify that usage of accelerating nodes makes the robot follow optimal paths in dynamic environments. © 2012 Rahul Kala et al.

关键词： Neural networks

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Multi Robot Exploration through Pruning Frontiers

Multi Robot Exploration through Pruning Frontiers

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International Conference on Material Science and Engineering Technology (ICMSET 2011)

作者： Pal, Anshika Tiwari, Ritu Shukla, Anupam Soft Computing and Expert System Laboratory ABV-Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9783037853610

In this paper, an approach to multi robot exploration is presented. One of the key issues in multi robot exploration is how to assign target locations to the individual robots and how to better distribute the robots over the environment. The proposed technique applies a well-known unsupervised clustering algorithm (k-means) in order to fairly divide the space into as many disjoint regions as available robots. Hungarian Method is used for the assignment of robots to the individual regions with the task to explore the corresponding area. To drive the robots around the environment, a frontier 'regions on the boundary between open space and unexplored space' based navigation strategy is used to decide where to move next, according to the data collected so far. Furthermore, we discuss improvements to the frontier based exploration strategy, by pruning the frontier cells that further reduces the computational time. The numbers of candidate locations are evaluated based on three criteria: number of unknown cells, number of known cells and real path travelling cost. Simulations are presented to show the performance of the proposed technique. This method can best be applied in search and rescue operations, partitioning helps to explore different regions of the workspace parallely by different robots instead of concentrating efforts in particular spot, pruning helps to make movement decisions much faster, the result is that the potential victims in a region will not have to wait much longer.

关键词： Multi-robot system Area exploration Unsupervised clustering k-means Hungarian method Frontiers

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Discrete cuckoo search optimization algorithm for combinatorial optimization of vehicle route in graph based road network 3

Discrete cuckoo search optimization algorithm for combinator...

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3rd International Conference on soft computing for Problem Solving, SocProS 2013

作者： Sur, Chiranjib Shukla, Anupam Soft Computing and Expert System Laboratory ABV-Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9788132217701

Cuckoo Search (CS) Algorithm is a well-known and successful nature inspired meta-heuristics which mimicries the salient life-style feature of cuckoo bird and has been widely applied in various continuous domain problems, search analysis and optimization. The algorithm mostly depends on the random placement of the constrained value(s) of variable at the solution set and is being evaluated by the fitness function. There is also provision for slow increment of the solution variable for local search. But here in this paper we have concentrated on the development and application of a modified version of the algorithm called Discrete Cuckoo Search Optimization Algorithm (DCSO) for discrete problem domain like that of the graph based problem and other combinatorial optimization problems like traveling salesman problem etc. The algorithm is first tested on the Travelling Salesman Problem benchmark datasets and then it is applied on a road based graph network for optimization with respect to a non-weighted fitness function of travel time and waiting time and is compared with Ant Colony Optimization (ACO) and Intelligent Water Drop (IWD). © Springer India 2014.

关键词： Combinatorial optimization

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Path Planning for Multiple Mobile Robots by Priority Assignment

Path Planning for Multiple Mobile Robots by Priority Assignm...

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International Conference on Computational Intelligence: Theories, Applications and Future Directions, ICCI 2017

作者： Dewangan, Ram Kishan Shukla, Anupam Wilfred Godfrey, W. Soft Computing and Expert System Laboratory ABV – Indian Institute of Information Technology and Management Gwalior Gwalior India

ISBN: (纸本)9789811311345

Priority assignment in the multi-robot path planning becomes a key challenge in the present situation. The motivation for the priority assignment is to utilize the search space for all the robots and minimize the overall path length. When the number of robots increases, the complexity also increases for the same search space. This paper gives the idea about how to assign the priority and choose the best minimum path length. The simulation results show the output performed and success rate of the robots. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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Egyptian vulture optimization algorithm - A new nature inspired meta-heuristics for knapsack problem

Egyptian vulture optimization algorithm - A new nature inspi...

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9th International Conference on computing and Information Technology, IC2IT 2013

作者： Sur, Chiranjib Sharma, Sanjeev Shukla, Anupam Soft Computing and Expert System Laboratory ABV-Indian Institute of Information Technology and Management Gwalior Madhya Pradesh 474010 India

ISBN: (纸本)9783642373701

In this paper we have introduced for the first time a new nature inspired meta-heuristics algorithm called Egyptian Vulture Optimization Algorithm which primarily favors combinatorial optimization problems. The algorithm is derived from the nature, behavior and key skills of the Egyptian Vultures for acquiring food for leading their livelihood. These spectacular, innovative and adaptive acts make Egyptian Vultures as one of the most intelligent of its kind among birds. The details of the bird's habit and the mathematical modeling steps of the algorithm are illustrated demonstrating how the meta-heuristics can be applied for global solutions of the combinatorial optimization problems and has been studied on the traditional 0/1 Knapsack Problem (KSP) and tested for several datasets of different dimensions. The results of application of the algorithm on KSP datasets show that the algorithm works well w.r.t optimal value and provide the scope of utilization in similar kind of problems like path planning and other combinatorial optimization problems. © 2013 Springer-Verlag.

关键词： Combinatorial optimization

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Solving travelling salesman problem using egyptian vulture optimization algorithm-A new approach

Solving travelling salesman problem using egyptian vulture o...

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20th International Conference on Language Processing and Intelligent Information systems, IIS 2013

作者： Sur, Chiranjib Sharma, Sanjeev Shukla, Anupam Soft Computing and Expert System Laboratory ABV Indian Institute of Information Technology and Management Gwalior Madhya Pradesh 474010 India

ISBN: (纸本)9783642386336

Travelling Salesman Problem (TSP) is a NP-Hard combinatorial optimization problem and many real life problems are constrained replica of it which possesses exponential time complexity and requires heavy combination capability. In this work a new nature inspired meta-heuristics called Egyptian Vulture Optimization Algorithm (EVOA) is being introduced and presented for the first time and illustrated with examples how it can be utilized for the constrained graph based problems and is utilized to solve the various dimensional datasets of the traditional travelling salesman problem. There are not many discrete optimization bio-inspired algorithms available in the literature and in that respect it is a novel one which can readily utilized for the graph based and assignment based problems. This EVOA is inspired by the natural and skilled phenomenal habits, unique perceptions and intelligence of the Egyptian Vulture bird for carry out the livelihood and acquisition of food which is inevitable for any kind of organisms. The Egyptian Vulture bird is one of the few birds who are known for showing dexterous capability when it comes to its confrontation with tough challenges and usage of tools with combinations of force and weakness finding ability. The results show that the Egyptian Vulture Optimization meta-heuristics has potential for deriving solutions for the TSP combinatorial problem and it is found that the quality and perfection of the solutions for the datasets depend mainly on the number of dimensions when considerable for the same number of iterations. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Combinatorial optimization

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Hybrid evolutionary approach for Devanagari handwritten numeral recognition using Convolutional Neural Network

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Procedia Computer Science 2018年 125卷 525-532页

作者： Adarsh Trivedi Siddhant Srivastava Apoorva Mishra Anupam Shukla Ritu Tiwari Soft Computing and Expert System Laboratory ABV-Indian Institute of Information Technology Gwalior 474015 India

In recent years, deep learning has been extensively used in both supervised and unsupervised learning problems. Among the deep learning models, CNN has outperformed all others for object recognition task. Although CNN achieves exceptional accuracy, still a huge number of iterations and chances of getting stuck in local optima makes it computationally expensive to train. Genetic Algorithm is a metaheuristic approach inspired by the theory of natural selection and has been used for solving both bounded and unbounded optimization problems by a large success. To handle these issues, we have developed a hybrid deep learning model using Genetic Algorithm and L-BFGS method for training CNN. To test our model, we have taken the Devanagari handwritten numeral dataset. Our results show that GA assisted CNN produces better results than non-GA assisted CNN. This study concludes that evolutionary technique can be used to train CNN more efficiently.

关键词： Convolutional Neural Network Genetic Algorithm Sparse Autoencoder

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Feature engineering combined with 1 D convolutional neural network for improved mortality prediction

arXiv

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arXiv 2019年

作者： Maheshwari, Saumil Verma, Rohit Shukla, Anupam Tiwari, Ritu Garg, Rishu Soft Computing and Expert System Laboratory National Institute of Technology Raipur India Soft Computing and Expert System Laboratory Atal Vihari Vajpayee- Indian Institute of Information Technology and Management GwaliorM.P. India

The intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of Electronic Health Record (EHR). This data allows for the development of a prediction tool with perfect knowledge backing. We aimed to build a mortality prediction model on 2012 Physionet Challenge mortality prediction database of 4000 patients admitted in ICU. The challenges in the dataset, such as high dimensionality, imbalanced distribution, and missing values were tackled with analytical methods and tools via feature engineering and new variable construction. The objective of the research is to utilize the relations among the clinical variables and construct new variables which would establish the effectiveness of 1-Dimensional Convolutional Neural Network (1-D CNN) with constructed features. Its performance with the traditional machine learning algorithms like XGBoost classifier, Support Vector Machine (SVM), K-Neighbours Classifier (K-NN), and Random Forest Classifier (RF) is compared for Area Under Curve (AUC). The investigation reveals the best AUC of 0.848 using 1-D CNN model. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Dealing QAP & KSP with Green Heron optimization algorithm — A new bio-inspired meta-heuristic

Dealing QAP & KSP with Green Heron optimization algorithm —...

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International Conference on computing and Networking Technology (ICCNT)

作者： Chiranjib Sur Anupam Shukla Soft Computing and Expert System Laboratory ABV-Indian Institute of Information Technology & Management Gwalior India

ISBN: (纸本)9781479939275

In this paper a new biological phenomenon following meta-heuristics called Green Heron Optimization Algorithm (GHOA) is being discussed, for the first time, which acquired its inspiration from the Green Heron birds, their intelligence, perception analysis capability and technique for food acquisition. The natural phenomenon of the bird has been capped into some unique operations which favour the graph based and discrete combinatorial optimization problems but with slight modification can also be utilized for other wide variety of problems of the real world which have discrete representation of data and variables having several constraints. In this work we have mainly concentrated on the description, mathematical representations, presentations, features, limitations and performance analysis of the algorithm on the scattered dimensional datasets of the Quadratic Assignment Problem (QAP) & 0/1 Knapsack Problem (KSP) to clearly demarcate its performance with change in dimension that is scalability. The results of the simulation clearly reveal how the algorithm has worked optimally for the various datasets of the problem. GHOA is one of the few members in the discrete domain algorithms of the bio-inspired computation family which favours suitably the graph based problems like path planning, process scheduling etc and has the capability of recombination and local search for global optimization and refinement of the solutions.

关键词： Optimization Birds Search problems Mathematical model Equations Algorithm design and analysis

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Area Exploration by Flocking of Multi Robot

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Procedia Engineering 2012年 41卷 377-382页

作者： Ashish Kumar Sanjeev Sharma Ritu Tiwari Samriddhi Majumdar Soft Computing and Expert System Laboratory Indian Institute of Information Technology and Management Gwalior 474015 India National Institute of Technology Agartala India

Area exploration is a widely known problem in the field of robotics. In the past decades wide range of research has been conducted for the area exploration problem. In recent years, the research ecclesiastical has focused on formulations for the efficient working of multiple robots. Through this research we intended to design a Multi robot flocking system which can explore the area or find the target. Here we have presented an accommodated approach for multi robot exploration, mapping and searching suitable for large teams of robot, function in unknown environment which lacking from an existing supporting communications framework. To design such a flock of robot, we have use flock steering rules for moving forward. To assign work, Bid value function have been used. This function will assign the area to explore, to each robot according to their bid value. So that collision could be avoided.

关键词： Multi robot Flocking Area exploration Bid value Weight

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